R/asthma_01_population.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title Asthma-01 Populations
#'
#' @description
#'
#' Filters data down to the target populations for Asthma-01, and categorizes
#' records to identify needed information for the calculations.
#'
#' Identifies key categories related to asthma-related incidents in an EMS
#' dataset, specifically focusing on cases where 911 was called for respiratory
#' distress, and certain medications were administered. This function segments
#' the data by age into adult and pediatric populations, computing the
#' proportion of cases that received beta-agonist treatment.
#'
#' @param df A data.frame or tibble containing EMS data. Default is `NULL`.
#' @param patient_scene_table A data.frame or tibble containing at least
#'   ePatient and eScene fields as a fact table. Default is `NULL`.
#' @param response_table A data.frame or tibble containing at least the
#'   eResponse fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data.frame or tibble containing at least the
#'   eSituation fields needed for this measure's calculations. Default is
#'   `NULL`.
#' @param medications_table A data.frame or tibble containing at least the
#'   eMedications fields needed for this measure's calculations. Default is
#'   `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#'   identifier. Default is `NULL`.
#' @param incident_date_col Column that contains the incident date. This
#'   defaults to `NULL` as it is optional in case not available due to PII
#'   restrictions.
#' @param patient_DOB_col Column that contains the patient's date of birth. This
#'   defaults to `NULL` as it is optional in case not available due to PII
#'   restrictions.
#' @param epatient_15_col Column representing the patient's numeric age agnostic
#'   of unit.
#' @param epatient_16_col Column representing the patient's age unit ("Years",
#'   "Months", "Days", "Hours", or "Minute").
#' @param eresponse_05_col Column that contains eResponse.05.
#' @param esituation_11_col Column that contains eSituation.11 provider primary
#' impression data.
#' @param esituation_12_col Column that contains all eSituation.12 values as
#'   (possible a single comma-separated list), provider secondary impression
#'   data.
#' @param emedications_03_col Column that contains all eMedications.03 values as
#'   a single comma-separated list.
#'
#' @return A list that contains the following:
#' * a tibble with counts for each filtering step,
#' * a tibble for each population of interest
#' * a tibble for the initial population
#' * a tibble for the total dataset with computations
#'
#' @examples
#'
#' # If you are sourcing your data from a SQL database connection
#' # or if you have your data in several different tables,
#' # you can pass table inputs versus a single data.frame or tibble
#'
#' # create tables to test correct functioning
#'
#' # patient table
#' patient_table <- tibble::tibble(
#'
#'   erecord_01 = 1:3,
#'   incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01")),
#'   patient_dob = as.Date(c("2000-01-01", "2020-01-01", "2023-01-01")),
#'   epatient_15 = c(25, 5, 2),
#'   epatient_16 = c("years", "years", "months")
#'
#' )
#'
#' # response table
#' response_table <- tibble::tibble(
#'
#'   erecord_01 = 1:3,
#'   eresponse_05 = c("2205001", "2205009", "2205003")
#'
#' )
#'
#' # situation table
#' situation_table <- tibble::tibble(
#'
#'   erecord_01 = 1:3,
#'   esituation_11 = c("weakness", "asthma", "bronchospasm"),
#'   esituation_12 = c("asthma", "weakness", "weakness")
#' )
#'
#' # medications table
#' medications_table <- tibble::tibble(
#'
#'   erecord_01 = 1:3,
#'   emedications_03 = c("albuterol", "levalbuterol", "metaproterenol")
#'
#' )
#'
#' # test the success of the function
#' result <- asthma_01_population(patient_scene_table = patient_table,
#'                                response_table = response_table,
#'                                situation_table = situation_table,
#'                                medications_table = medications_table,
#'                                erecord_01_col = erecord_01,
#'                                incident_date_col = incident_date,
#'                                patient_DOB_col = patient_dob,
#'                                epatient_15_col = epatient_15,
#'                                epatient_16_col = epatient_16,
#'                                eresponse_05_col = eresponse_05,
#'                                esituation_11_col = esituation_11,
#'                                esituation_12_col = esituation_12,
#'                                emedications_03_col = emedications_03
#'                                )
#'
#' # show the results of filtering at each step
#' result$filter_process
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
asthma_01_population <- function(df = NULL,
                                 patient_scene_table = NULL,
                                 response_table = NULL,
                                 situation_table = NULL,
                                 medications_table = NULL,
                                 erecord_01_col,
                                 incident_date_col = NULL,
                                 patient_DOB_col = NULL,
                                 epatient_15_col,
                                 epatient_16_col,
                                 eresponse_05_col,
                                 esituation_11_col,
                                 esituation_12_col,
                                 emedications_03_col) {

  # ensure that not all table arguments AND the df argument are fulfilled
  # user only passes df or all table arguments

    if(

    any(
      !is.null(patient_scene_table),
      !is.null(response_table),
      !is.null(situation_table),
      !is.null(medications_table)
    )

    &&

    !is.null(df)

  ) {

    cli::cli_abort("{.fn asthma_01_population} will only work by passing a {.cls data.frame} or {.cls tibble} to the {.var df} argument, or by fulfilling all table arguments.  Please choose to either pass an object of class {.cls data.frame} or {.cls tibble} to the {.var df} argument, or fulfill all table arguments.")

  }

  # ensure that df or all table arguments are fulfilled

  if(

    all(
      is.null(patient_scene_table),
      is.null(response_table),
      is.null(situation_table),
      is.null(medications_table)
    )

    && is.null(df)

  ) {

    cli::cli_abort("{.fn asthma_01_population} will only work by passing a {.cls data.frame} or {.cls tibble} to the {.var df} argument, or by fulfilling all table arguments.  Please choose to either pass an object of class {.cls data.frame} or {.cls tibble} to the {.var df} argument, or fulfill all table arguments.")

  }

  # ensure all *_col arguments are fulfilled
  if(

    any(

      missing(erecord_01_col),
      missing(incident_date_col),
      missing(patient_DOB_col),
      missing(epatient_15_col),
      missing(epatient_16_col),
      missing(eresponse_05_col),
      missing(esituation_11_col),
      missing(esituation_12_col),
      missing(emedications_03_col)
    )

  ) {

    cli::cli_abort("One or more of the *_col arguments is missing.  Please make sure you pass an unquoted column to each of the *_col arguments to run {.fn asthma_01_population}.")

  }

  # 911 codes for eresponse.05
  codes_911 <- "2205001|2205003|2205009|Emergency Response \\(Primary Response Area\\)|Emergency Response \\(Intercept\\)|Emergency Response \\(Mutual Aid\\)"

  # get codes as a regex to filter primary/secondary impression fields
  beta_agonist <- "435|7688|214199|237159|487066|1154062|1163444|1649559|1165719|2108209|2108252|albuterol|ipratropium|levalbuterol|metaproterenol"

  # codes for asthma or acute bronchospasm
  asthma_codes <- "(?:J45|J98.01)|asthma|acute bronchospasm"

  year_values <- "2516009|years"

  day_values <- "days|2516001"

  hour_values <- "hours|2516003"

  minute_values <- "minutes|2516005"

  month_values <- "months|2516007"

  # options for the progress bar
  # a green dot for progress
  # a white line for note done yet
  options(cli.progress_bar_style = "dot")

  options(cli.progress_bar_style = list(
    complete = cli::col_green("\u25CF"),  # Black Circle
    incomplete = cli::col_br_white("\u2500")  # Light Horizontal Line
  ))

  # initiate the progress bar process
  progress_bar_population <- cli::cli_progress_bar(
    "Running `asthma_01_population()`",
    total = 10,
    type = "tasks",
    clear = F,
    format = "{cli::pb_name} [Working on {cli::pb_current} of {cli::pb_total} tasks] {cli::pb_bar} | {cli::col_blue('Progress')}: {cli::pb_percent} | {cli::col_blue('Runtime')}: [{cli::pb_elapsed}]"
  )

  # utilize applicable tables to analyze the data for the measure
  if(
    all(!is.null(patient_scene_table),
        !is.null(response_table),
        !is.null(situation_table),
        !is.null(medications_table)
    ) && is.null(df)
  ) {

    if(!(
      (is.data.frame(patient_scene_table) && tibble::is_tibble(patient_scene_table)) ||

      (is.data.frame(response_table) && tibble::is_tibble(response_table)) ||

      (is.data.frame(situation_table) && tibble::is_tibble(situation_table)) ||

      (is.data.frame(medications_table) && tibble::is_tibble(medications_table))

    )
    ) {

      cli::cli_abort("One or more of the tables passed to {.fn asthma_01_population} were not of class {.cls data.frame} nor {.cls tibble}. When passing multiple tables to {.fn asthma_01_population}, all tables must be of class {.cls data.frame} or {.cls tibble}.")

    }

    # only check the date columns if they are in fact passed
    if(
      all(
        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
      )
    )

      {
    # use quasiquotation on the date variables to check format
    incident_date <- rlang::enquo(incident_date_col)
    patient_dob <- rlang::enquo(patient_DOB_col)

    if ((!lubridate::is.Date(patient_scene_table[[rlang::as_name(incident_date)]]) &
         !lubridate::is.POSIXct(patient_scene_table[[rlang::as_name(incident_date)]])) ||
        (!lubridate::is.Date(patient_scene_table[[rlang::as_name(patient_dob)]]) &
         !lubridate::is.POSIXct(patient_scene_table[[rlang::as_name(patient_dob)]]))) {

      cli::cli_abort(
        "For the variables {.var incident_date_col} and {.var patient_DOB_col}, one or both of these variables were not of class {.cls Date} or a similar class.  Please format your {.var incident_date_col} and {.var patient_DOB_col} to class {.cls Date} or similar class."
      )

      }
  }

    progress_bar_population

    # progress update, these will be repeated throughout the script
    cli::cli_progress_update(set = 1, id = progress_bar_population, force = TRUE)

    ###_____________________________________________________________________________
    # fact table
    # the user should ensure that variables beyond those supplied for calculations
    # are distinct (i.e. one value or cell per patient)
    ###_____________________________________________________________________________

    # progress update, these will be repeated throughout the script

    if(all(

      !rlang::quo_is_null(rlang::enquo(incident_date_col)),
      !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
    )) {

    # filter the table to get the initial population ages >= 2 years
    final_data <- patient_scene_table |>

      # create the age in years variable
      dplyr::mutate(patient_age_in_years_col = as.numeric(difftime(
        time1 = {{  incident_date_col  }},
        time2 = {{ patient_DOB_col }},
        units = "days"
      )) / 365,

      # system age check
      system_age_adult = {{  epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor1 = ({{  epatient_15_col }} < 18 & {{  epatient_15_col }} >= 2) & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor2 = {{ epatient_15_col}} >= 24 & grepl(pattern = month_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor = system_age_minor1 | system_age_minor2,

      # calculated age check
      calc_age_adult = patient_age_in_years_col >= 18,
      calc_age_minor = patient_age_in_years_col < 18

      ) |>
      dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE)

    } else if(

      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )) {

      # filter the table to get the initial population ages >= 2 years
      final_data <- patient_scene_table |>

        # create the age in years variable
        dplyr::mutate(

        # system age check
        system_age_adult = {{  epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
        system_age_minor1 = ({{  epatient_15_col }} < 18 & {{  epatient_15_col }} >= 2) & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
        system_age_minor2 = {{ epatient_15_col}} >= 24 & grepl(pattern = month_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
        system_age_minor = system_age_minor1 | system_age_minor2


        ) |>
        dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE)


    }

    ###_____________________________________________________________________________
    ### dimension tables
    ### each dimension table is turned into a vector of unique IDs
    ### that are then utilized on the fact table to create distinct variables
    ### that tell if the patient had the characteristic or not for final
    ### calculations of the numerator and filtering
    ###_____________________________________________________________________________

    cli::cli_progress_update(set = 2, id = progress_bar_population, force = TRUE)

    # 911 calls
    call_911_data <- response_table |>
      dplyr::select({{ erecord_01_col }}, {{ eresponse_05_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(

        grepl(pattern = codes_911, x = {{ eresponse_05_col }}, ignore.case = TRUE)

      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(set = 3, id = progress_bar_population, force = TRUE)

    # Identify Records that have specified asthma
    asthma_data <- situation_table |>
      dplyr::select({{ erecord_01_col }}, {{ esituation_11_col}}, {{esituation_12_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(

        dplyr::if_any(

          c({{ esituation_11_col}}, {{esituation_12_col }}), ~ grepl(pattern = asthma_codes, x = ., ignore.case = TRUE)

        )) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(set = 4, id = progress_bar_population, force = TRUE)

    # check to ensure beta agonist was used
    beta_agonist_data <- medications_table |>
      dplyr::select({{ erecord_01_col }}, {{ emedications_03_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(

        grepl(pattern = beta_agonist, x = {{ emedications_03_col }}, ignore.case = TRUE)

      ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(set = 5, id = progress_bar_population, force = TRUE)

    # get the computing population that is the full dataset with identified categories
    computing_population <- final_data |>
      dplyr::mutate(call_911 = {{ erecord_01_col }} %in% call_911_data,
                    asthma = {{ erecord_01_col }} %in% asthma_data,
                    beta_agonist_check = {{ erecord_01_col }} %in% beta_agonist_data
      )

    cli::cli_progress_update(set = 6, id = progress_bar_population, force = TRUE)

    # get the initial population
    initial_population <- computing_population |>
      dplyr::filter(


        # asthma patients
        asthma,

        # 911 calls
        call_911

      )

    # Adult and Pediatric Populations

    cli::cli_progress_update(set = 7, id = progress_bar_population, force = TRUE)

    if(

      # use the system generated and calculated ages

      all(

        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))

      )) {

    # filter adult
    adult_pop <- initial_population |>
      dplyr::filter(calc_age_adult | system_age_adult)

    cli::cli_progress_update(set = 8, id = progress_bar_population, force = TRUE)

    # filter peds
    peds_pop <- initial_population |>
      dplyr::filter(system_age_minor | calc_age_minor)

    } else if(

      # only use the system generated values

      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )) {

      # filter adult
      adult_pop <- initial_population |>
        dplyr::filter(system_age_adult)

      cli::cli_progress_update(set = 8, id = progress_bar_population, force = TRUE)

      # filter peds
      peds_pop <- initial_population |>
        dplyr::filter(system_age_minor)


    }

    cli::cli_progress_update(set = 9, id = progress_bar_population, force = TRUE)

    # summarize counts for populations filtered
    filter_counts <- tibble::tibble(
      filter = c("911 calls",
                 "Asthma cases",
                 "Beta agonist cases",
                 "Adults denominator",
                 "Peds denominator",
                 "Initial population",
                 "Total dataset"
      ),
      count = c(
        sum(computing_population$call_911, na.rm = TRUE),
        sum(computing_population$asthma, na.rm = TRUE),
        sum(computing_population$beta_agonist_check, na.rm = TRUE),
        nrow(adult_pop),
        nrow(peds_pop),
        nrow(initial_population),
        nrow(computing_population)
      )
    )

    # get the populations of interest

    cli::cli_progress_update(set = 10, id = progress_bar_population, force = TRUE)

    # gather data into a list for multi-use output
    asthma.01.population <- list(
      filter_process = filter_counts,
      adults = adult_pop,
      peds = peds_pop,
      initial_population = initial_population,
      computing_population = computing_population
    )

    cli::cli_progress_done(id = progress_bar_population)

    return(asthma.01.population)


  } else if(

    all(is.null(patient_scene_table), is.null(response_table), is.null(situation_table), is.null(medications_table)) && !is.null(df)

    )

    # utilize a dataframe to analyze the data for the measure analytics

  {

    # Ensure df is a data frame or tibble
    if (!is.data.frame(df) && !tibble::is_tibble(df)) {
      cli::cli_abort(
        c(
          "An object of class {.cls data.frame} or {.cls tibble} is required as the first argument.",
          "i" = "The passed object is of class {.val {class(df)}}."
        )
      )
    }

    # only check the date columns if they are in fact passed
    if(
      all(
        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))
      )
    )

      {

    # use quasiquotation on the date variables to check format
    incident_date <- rlang::enquo(incident_date_col)
    patient_dob <- rlang::enquo(patient_DOB_col)

    if ((!lubridate::is.Date(df[[rlang::as_name(incident_date)]]) &
         !lubridate::is.POSIXct(df[[rlang::as_name(incident_date)]])) ||
        (!lubridate::is.Date(df[[rlang::as_name(patient_dob)]]) &
         !lubridate::is.POSIXct(df[[rlang::as_name(patient_dob)]]))) {

      cli::cli_abort(
        "For the variables {.var incident_date_col} and {.var patient_DOB_col}, one or both of these variables were not of class {.cls Date} or a similar class.  Please format your {.var incident_date_col} and {.var patient_DOB_col} to class {.cls Date} or similar class."
      )

    }
  }

    progress_bar_population

    # progress update, these will be repeated throughout the script
    cli::cli_progress_update(set = 1, id = progress_bar_population, force = TRUE)

    ###_____________________________________________________________________________
    # from the full dataframe with all variables
    # create one fact table and several dimension tables
    # to complete calculations and avoid issues due to row
    # explosion
    ###_____________________________________________________________________________

    # fact table
    # the user should ensure that variables beyond those supplied for calculations
    # are distinct (i.e. one value or cell per patient)

    if(

      # use the system generated and calculated ages

      all(

        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))

      )) {

    # filter the table to get the total dataset with identified categories
    final_data <- df |>
      dplyr::select(-c({{  eresponse_05_col  }},
                       {{  esituation_11_col  }},
                       {{  esituation_12_col }},
                       {{  emedications_03_col  }}

      )) |>
      dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
      # create the age in years variable

      dplyr::mutate(patient_age_in_years_col = as.numeric(difftime(
        time1 = {{  incident_date_col  }},
        time2 = {{ patient_DOB_col }},
        units = "days"
      )) / 365,

      # system age check
      system_age_adult = {{  epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor1 = ({{  epatient_15_col }} < 18 & {{  epatient_15_col }} >= 2) & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor2 = {{ epatient_15_col}} >= 24 & grepl(pattern = month_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor = system_age_minor1 | system_age_minor2,

      # calculated age check
      calc_age_adult = patient_age_in_years_col >= 18,
      calc_age_minor = patient_age_in_years_col < 18

      )

  } else if(

  # only use the system generated values

  all(
    is.null(incident_date_col),
    is.null(patient_DOB_col)
  )) {

  # filter the table to get the total dataset with identified categories
    final_data <- df |>
      dplyr::select(-c({{  eresponse_05_col  }},
                       {{  esituation_11_col  }},
                       {{  esituation_12_col }},
                       {{  emedications_03_col  }}

      )) |>
      dplyr::distinct({{ erecord_01_col }}, .keep_all = TRUE) |>
      # create the age in years variable

      dplyr::mutate(

      # system age check
      system_age_adult = {{  epatient_15_col }} >= 18 & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor1 = ({{  epatient_15_col }} < 18 & {{  epatient_15_col }} >= 2) & grepl(pattern = year_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor2 = {{ epatient_15_col}} >= 24 & grepl(pattern = month_values, x = {{ epatient_16_col}}, ignore.case = TRUE),
      system_age_minor = system_age_minor1 | system_age_minor2

      )

  }

    ###_____________________________________________________________________________
    ### dimension tables
    ### each dimension table is turned into a vector of unique IDs
    ### that are then utilized on the fact table to create distinct variables
    ### that tell if the patient had the characteristic or not for final
    ### calculations of the numerator and filtering
    ###_____________________________________________________________________________

    cli::cli_progress_update(set = 2, id = progress_bar_population, force = TRUE)

    # 911 calls
    call_911_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{  eresponse_05_col  }}) |>
      dplyr::distinct() |>
      dplyr::filter(grepl(pattern = codes_911, x = {{  eresponse_05_col  }}, ignore.case = TRUE)) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(set = 3, id = progress_bar_population, force = TRUE)

    # asthma population
    asthma_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{  esituation_11_col  }}, {{ esituation_12_col }}) |>
      dplyr::distinct() |>
      dplyr::filter(

        # Identify Records that have specified asthma
        dplyr::if_any(c({{ esituation_11_col}}, {{esituation_12_col }}), ~ grepl(pattern = asthma_codes, x = ., ignore.case = TRUE))

        ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(set = 4, id = progress_bar_population, force = TRUE)

    # beta agonist use
    beta_agonist_data <- df |>
      dplyr::select({{ erecord_01_col }}, {{  emedications_03_col  }}) |>
      dplyr::distinct() |>
      dplyr::filter(

        # check to ensure beta agonist was used
        grepl(pattern = beta_agonist, x = {{ emedications_03_col }}, ignore.case = TRUE)

        ) |>
      dplyr::distinct({{ erecord_01_col }}) |>
      dplyr::pull({{ erecord_01_col }})

    cli::cli_progress_update(set = 5, id = progress_bar_population, force = TRUE)

    # get the computing population that is the full dataset with identified categories
    computing_population <- final_data |>
      dplyr::mutate(call_911 = {{ erecord_01_col }} %in% call_911_data,
                    asthma = {{ erecord_01_col }} %in% asthma_data,
                    beta_agonist_check = {{ erecord_01_col }} %in% beta_agonist_data
      )

    cli::cli_progress_update(set = 6, id = progress_bar_population, force = TRUE)

    # get the initial population
    initial_population <- computing_population |>
      dplyr::filter(


        # asthma patients
        asthma,

        # 911 calls
        call_911

      )

    # Adult and Pediatric Populations

    cli::cli_progress_update(set = 7, id = progress_bar_population, force = TRUE)

    if(

      # use the system generated and calculated ages

      all(

        !rlang::quo_is_null(rlang::enquo(incident_date_col)),
        !rlang::quo_is_null(rlang::enquo(patient_DOB_col))

      )) {

    # filter adult
    adult_pop <- initial_population |>
      dplyr::filter(calc_age_adult | system_age_adult)

    cli::cli_progress_update(set = 8, id = progress_bar_population, force = TRUE)

    # filter peds
    peds_pop <- initial_population |>
      dplyr::filter(system_age_minor | calc_age_minor)

    } else if(

      # only use the system generated values

      all(
        is.null(incident_date_col),
        is.null(patient_DOB_col)
      )) {

      # filter adult
      adult_pop <- initial_population |>
        dplyr::filter(system_age_adult)

      cli::cli_progress_update(set = 8, id = progress_bar_population, force = TRUE)

      # filter peds
      peds_pop <- initial_population |>
        dplyr::filter(system_age_minor)

    }

    cli::cli_progress_update(set = 9, id = progress_bar_population, force = TRUE)

    # summarize counts for populations filtered
    filter_counts <- tibble::tibble(
      filter = c("911 calls",
                 "Asthma cases",
                 "Beta agonist cases",
                 "Adults denominator",
                 "Peds denominator",
                 "Initial population",
                 "Total dataset"
      ),
      count = c(
        length(call_911_data),
        length(asthma_data),
        length(beta_agonist_data),
        nrow(adult_pop),
        nrow(peds_pop),
        nrow(initial_population),
        nrow(computing_population)
      )
    )

    # get the populations of interest

    cli::cli_progress_update(set = 10, id = progress_bar_population, force = TRUE)

    # gather data into a list for multi-use output
    asthma.01.population <- list(
      filter_process = filter_counts,
      adults = adult_pop,
      peds = peds_pop,
      initial_population = initial_population,
      computing_population = computing_population
    )

    cli::cli_progress_done(id = progress_bar_population)

    return(asthma.01.population)

  }

}